---
title: "Climate Risk Lab IVU Dash"
author: ""
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    vertical_layout: fill
    theme: cerulean
---

```{r setup, include=FALSE}

# Please ensure your data files are in the working directory before running the code

# Importing data
transition_risk_data = read.csv("Preparedness_of_countries_for_a_low-carbon_transition.csv")
physical_risk_data = read.csv("Climate-driven_INFORM_Risk.csv")
financial_risk_data = read.csv("Carbon_footprint_adjusted_loans_to_total_loans_for_deposit_takers.csv")


# Calling packages
suppressPackageStartupMessages(c(library(flexdashboard),
library(shinythemes),
library(tidyverse),
library(plotly),
library(maps),
library(leaflet),
library(leafpop),
library(purrr),
library(knitr),
library(DT)))


# Defining color pallete 
#colors_palette = c("green4", "#6cc71c", "lawngreen", "greenyellow", "#adf20c", "#eff22e", "gold", "gold3", "#c75e1c", "#f52525", "brown")
#colors_palette = c("forestgreen", "limegreen", "green", "olivedrab1", "greenyellow", "yellow", "yellow3", "gold", "firebrick1", "red", "red4")

# Creating subsets
transition_risk_data = transition_risk_data %>% 
    dplyr::group_by(Country, Indicator)

transition_risk_data_exposure = transition_risk_data %>% 
    filter(Indicator == "Exposure")

transition_risk_data_resilience = transition_risk_data %>% 
    filter(Indicator == "Resilience")


# Light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)

g <- list(
    showframe = F,
    showcoastlines = TRUE, 
    coastlinecolor = "Black",
    showland = TRUE,
    landcolor = "White",
    showocean= TRUE, 
    oceancolor ="deepskyblue",
    showlakes= TRUE, 
    lakecolor ="deepskyblue",
    showrivers= TRUE, 
    rivercolor ="deepskyblue",
    projection = list(type = 'Mercator')
)

```

Row {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Exposure to Transition Risk

```{r}

plot_exposure <- plot_geo(transition_risk_data_exposure)
plot_exposure %>% add_trace(
    z = ~ transition_risk_data_exposure$F2019, 
    color = ~ transition_risk_data_exposure$F2019, 
    colors = 'Blues',
    text = ~ transition_risk_data_exposure$Country, 
    locations = ~ transition_risk_data_exposure$ISO3, 
    marker = list(line = l)) %>% 
    colorbar(title = 'Exposure Index (as on 31-12-2019)', tickprefix = '') %>% 
    layout(
    title = 'Exposure to Low-Carbon Economy Transition',
    xaxis = list(title = 'Scale from 0 (high resilience) to 1 (low resilience)'),
    geo = g
)

```

### Resilience to Transition Risk

```{r}

plot_resilience <- plot_geo(transition_risk_data_resilience)
plot_resilience %>% add_trace(
    z = ~ transition_risk_data_resilience$F2019, 
    color = ~ transition_risk_data_resilience$F2019, 
    colors = 'Blues',
    text = ~ transition_risk_data_resilience$Country, 
    locations = ~ transition_risk_data_resilience$ISO3, 
    marker = list(line = l)) %>% 
    colorbar(title = 'Resilience Index (as on 31-12-2019)', tickprefix = '') %>% 
    layout(
    title = 'Resilience to Low-Carbon Economy Transition',
    xaxis = list(title = 'Scale from 0 (high resilience) to 1 (low resilience)'),
    geo = g
)

```